CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2012; 24:880–894 Published online 7 July 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.1778 SPECIAL ISSUE PAPER Rapid computation of value and risk for derivatives portfolios Stephen Weston 1,2, * ,† , James Spooner 3 , Sébastien Racanière 3 and Oskar Mencer 3 1 Credit Quantitative Research, J.P. Morgan, London EC2Y 5AJ, U.K. 2 The University of Warwick, Coventry CV4 7AL, U.K. 3 Maxeler Technologies, Imperial College, London W6 9JH, U.K. SUMMARY We report new results from an on-going project to accelerate derivatives computations. Our earlier work was focused on accelerating the valuation of credit derivatives. In this paper, we extend our work in two ways: by applying the same techniques, first, to accelerate the computation of portfolio level risk for credit derivatives and, second, to different asset classes using a different type of mathematical model, which together present challenges that are quite different to those dealt with in our earlier work. Specifically, we report acceleration over 270 times faster than a single Intel Core for a multi-asset Monte Carlo model. We also explore the implications for risk. Copyright © 2011 John Wiley & Sons, Ltd. Received 27 March 2011; Accepted 2 April 2011 KEY WORDS: FPGA; J.P. Morgan; Maxeler; acceleration; credit derivatives; Monte Carlo 1. INTRODUCTION One of the key enablers of the growth and innovation in the global derivatives markets has been the development and intensive use of complex mathematical models. As the use of derivative instru- ments based on such models has expanded across asset classes, the process of valuing and managing the risk of such complex portfolios has grown to a point where thousands of CPU cores are used for the daily calculation of value and risk. Unfortunately, CPU cores consume vast amounts of electricity both for powering the CPUs themselves as well as for cooling. In 2005, the world’s estimated 27 million servers consumed around 0.5% of all electricity pro- duced on the planet, a figure that is closer to 1% when the energy for associated cooling and auxiliary equipment (e.g., backup power, power conditioning, power distribution, air handling, lighting, and chillers) is included [1]. Although it is true that the purchase costs of hardware are falling, such savings are being increasingly offset by rapidly rising power-related indirect costs [2]. These costs have led many large financial institutions to search for ways to continue to add greater computational power, with reduced capital and operating costs. In this paper, we follow on from our earlier work [3] and report new results from the collab- orative project between J.P. Morgan in London and the acceleration solutions provider Maxeler Technologies, based on work that took place during the period from late 2009 through to the end of 2010. All of the computations reported in this paper were carried out on the MaxRack hybrid (field-programmable gate array (FPGA) and Intel CPU) cluster solution delivered by Maxeler. The contributions of this new paper therefore are as follows: *Correspondence to: Stephen Weston, Credit Quantitative Research, J.P. Morgan, London EC2Y 5AJ, U.K. † E-mail: stephen.p.weston@jpmorgan.com Copyright © 2011 John Wiley & Sons, Ltd.